CN117638168A - Multi-core chip-based optimal control method for fuel cell thermal management system - Google Patents
Multi-core chip-based optimal control method for fuel cell thermal management system Download PDFInfo
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Abstract
The invention relates to a fuel cell thermal management system optimization control method based on a multi-core chip, which comprises the following steps: a fuel cell controller based on a multi-core processor distributes tasks of a fuel cell thermal management system to three cores CPU0, CPU1 and CPU2 of the processor in parallel; the fuel cell energy efficiency optimization method is distributed to a first core CPU0 of the multi-core processor in parallel; distributing a fuel cell thermal management system control algorithm to a second core CPU1 of the multi-core processor for parallel; the diagnosis and monitoring tasks of the fuel cell thermal management system are distributed to a third core CPU2 of the multi-core processor for parallel; the invention provides a fuel cell thermal management method combining temperature control and energy efficiency improvement; the energy-efficient state of the fuel cell can be maintained in different environments. The invention provides a method for realizing task parallelism by utilizing a multi-core processor for a thermal management system, which reduces the running time of a program and can improve the real-time performance of control.
Description
Technical Field
The invention relates to the technical field of fuel cells, in particular to a multi-core chip-based optimal control method for a fuel cell thermal management system.
Background
The fuel cell is a device for directly converting chemical energy into electric energy, has the advantages of small environmental pollution, high power density and the like, and has a large application prospect in the fields of automobiles and the like. The energy conversion efficiency of the fuel cell is about 50%, and about 50% of the energy is emitted as heat energy. If the heat dissipation is poor, the highest temperature exceeding the design temperature of the fuel cell will have an impact on the overall efficiency, life and stability of the fuel cell. Therefore, thermal management systems are needed to ensure that the fuel cell operates within a reliable and efficient temperature range.
At present, in the technical field of vehicle-mounted fuel cell thermal management systems, a thermal management system for a fuel cell focuses on the heat dissipation capability of the thermal management system. The research on the temperature control method is single, and the response to different temperature environments can not be performed, so that the temperature control effect of the fuel cell is affected. Secondly, the heat dissipation fan, the water pump and other accessories of the thermal management system consume larger power, the temperature of the fuel cell is ensured, the net output power of the fuel cell and the thermal management system is maximized, and the energy efficiency of the fuel cell system is also needed to be researched.
Disclosure of Invention
Aiming at the problems, the invention provides an optimized control method of a fuel cell thermal management system based on a multi-core chip.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an optimized control method of a fuel cell thermal management system based on a multi-core chip, the method comprising the steps of:
a fuel cell controller based on a multi-core processor distributes tasks of a fuel cell thermal management system to three cores CPU0, CPU1 and CPU2 of the processor in parallel;
the fuel cell energy efficiency optimization method is distributed to a first core CPU0 of the multi-core processor in parallel, and the optimal temperature value calculated by the energy efficiency optimization algorithm is written into a shared memory through the CPU 0;
distributing a fuel cell thermal management system control algorithm to a second core CPU1 of the multi-core processor in parallel, and continuously correcting a control target by reading an optimal temperature value updated in real time in a shared memory through the CPU 1;
and in parallel, the diagnosis and monitoring task of the fuel cell thermal management system is distributed to a third core CPU2 of the multi-core processor, and the current temperature of the fuel cell is monitored and diagnosed through the CPU2, so as to judge whether to stop the operation of the fuel cell.
As a further technical solution of the present invention, the step of writing the optimal temperature value calculated by the energy efficiency optimization algorithm into the shared memory by the CPU0 includes:
acquiring the environmental temperature of the fuel cell in real time;
acquiring load current of the fuel cell in real time;
acquiring the output power of the fuel cell in real time;
acquiring the consumption power of a thermal management system accessory in real time, wherein the thermal management system accessory comprises a fan, a water pump and a thermostat;
acquiring the current temperature of the fuel cell in real time;
in the normal working temperature range of the fuel cell, with the aim of maximizing the total energy efficiency, calculating an optimal temperature value in the current state on line according to the environment temperature, the load current, the output power, the consumption power and the current temperature of the fuel cell through an energy efficiency optimization algorithm;
judging whether the optimal temperature value calculated by the energy efficiency optimization algorithm exceeds a normal working temperature threshold value of the fuel cell;
if the optimal temperature value does not exceed the normal working temperature threshold value of the fuel cell, writing the optimal temperature value into a shared memory;
triggering an interrupt to wait for the CPU1 to read the shared memory data;
if the CPU1 finishes reading the shared memory data, clearing the interrupt;
and entering the next cycle.
As a further technical solution of the present invention, the total energy efficiency is determined by the following formula:
；
wherein P is tot Representing total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
As a further technical solution of the present invention, the total energy efficiency is determined by the following formula:
wherein η represents total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
As a further technical scheme of the invention, the energy efficiency optimization algorithm adopts a genetic algorithm.
As a further technical solution of the present invention, the step of continuously correcting the control target by reading the optimal temperature value updated in real time in the shared memory by the CPU1 includes:
acquiring the current temperature of the fuel cell;
triggering an interrupt and starting timing;
CPU1 reads the real-time updated optimal temperature value in the shared memory;
if the CPU0 does not update the data, waiting for the CPU0 to update the data;
if the preset time is waited, the last time of reading the optimal temperature value is used, and the interruption is cleared;
if the CPU0 updates the data in the timing time, reading the updated optimal temperature value, and clearing the interrupt;
ending the timing;
taking the read latest optimal temperature value as a temperature target for controlling the fuel cell, and calculating accessory parameters of a thermal management system by a thermal management system control algorithm, wherein the accessory parameters of the thermal management system comprise the rotating speed of a cooling fan, the rotating speed of a water pump and the opening degree of a thermostat;
and controlling the accessory of the thermal management system according to the thermal management system accessory parameters calculated by the thermal management system control algorithm.
As a further technical scheme of the invention, the control algorithm of the thermal management system adopts a self-adaptive fuzzy PID algorithm.
As a further technical solution of the present invention, the step of diagnosing and monitoring the current temperature of the fuel cell by the CPU2, and judging whether to stop the operation of the fuel cell includes:
acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell does not exceed the temperature early warning value, returning to the step of acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell exceeds the temperature early warning value, taking over the control of the fuel cell thermal management system;
the rotation speeds of the cooling fan and the water pump are adjusted to be maximum, and the thermostat is fully opened;
if the current temperature of the fuel cell does not exceed the threshold value, returning to the step of acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell exceeds the threshold, the fuel cell stops operating.
Compared with the prior art, the invention has the beneficial effects that: the invention provides an optimized control method of a fuel cell thermal management system based on a multi-core chip, and provides a fuel cell thermal management method combining temperature control and energy efficiency improvement; the energy-efficient state of the fuel cell can be maintained in different environments. The invention provides a method for realizing task parallelism by utilizing a multi-core processor for a thermal management system, which reduces the running time of a program and can improve the real-time performance of control.
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FIG. 1 is a flow chart of a method for optimizing control of a fuel cell thermal management system based on a multi-core chip.
Detailed Description
The technical scheme of the patent is further described in detail below with reference to the specific embodiments.
The embodiment of the invention provides a fuel cell thermal management system optimization control method based on a multi-core chip, which comprises the following steps:
1) A fuel cell controller based on a multi-core processor distributes tasks of a fuel cell thermal management system to three cores CPU0, CPU1 and CPU2 of the processor in parallel;
2) The fuel cell energy efficiency optimization method is distributed to a first core CPU0 of the multi-core processor in parallel, and the optimal temperature value calculated by the energy efficiency optimization algorithm is written into a shared memory through the CPU 0;
2.1 Acquiring the environmental temperature of the fuel cell in real time;
2.2 Acquiring the load current of the fuel cell in real time;
2.3 Acquiring the output power of the fuel cell in real time;
2.4 The power consumption of the thermal management system accessories is obtained in real time, wherein the thermal management system accessories comprise fans, water pumps and thermostats;
2.5 Acquiring the current temperature of the fuel cell in real time;
2.6 In the normal working temperature range of the fuel cell, with the aim of maximizing the total energy efficiency, calculating an optimal temperature value in the current state on line according to the environment temperature, the load current, the output power, the consumption power and the current temperature of the fuel cell by an energy efficiency optimization algorithm;
in this embodiment, a genetic algorithm is adopted as the energy efficiency optimization algorithm.
In this embodiment, the total energy efficiency is determined by the following formula:
；
wherein P is tot Representing total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
In this embodiment, the total energy efficiency may also be determined by the following formula:
wherein η represents total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
2.7 Judging whether the optimal temperature value calculated by the energy efficiency optimization algorithm exceeds a normal operating temperature threshold of the fuel cell;
2.8 If the optimal temperature value does not exceed the normal working temperature threshold of the fuel cell, writing the optimal temperature value into a shared memory;
2.9 If the optimal temperature value exceeds the normal operating temperature threshold of the fuel cell, replacing the calculated temperature value with a value within a preset normal operating temperature range;
2.10 Triggering an interrupt, waiting for the CPU1 to read the shared memory data;
2.11 If the CPU1 finishes reading the shared memory data, clearing the interrupt;
2.12 A) enters the next cycle.
3) Distributing a fuel cell thermal management system control algorithm to a second core CPU1 of the multi-core processor in parallel, and continuously correcting a control target by reading an optimal temperature value updated in real time in a shared memory through the CPU 1;
3.1 Acquiring a current temperature of the fuel cell;
3.2 Triggering an interrupt and starting timing;
3.3 CPU1 reads the real-time updated optimal temperature value in the shared memory;
3.4 If the CPU0 does not update the data, waiting for the CPU0 to update the data;
3.5 If the time is more than the preset time, the last read optimal temperature value is used to clear the interrupt;
3.6 If the CPU0 updates the data in the timing time, reading the updated optimal temperature value, and clearing the interrupt;
3.7 Ending the timer;
3.8 Taking the latest optimal temperature value as a temperature target for controlling the fuel cell, and calculating accessory parameters of the thermal management system by a control algorithm of the thermal management system, wherein the accessory parameters of the thermal management system comprise the rotating speed of a cooling fan, the rotating speed of a water pump and the opening degree of a thermostat;
3.9 And controlling the accessory of the thermal management system according to the thermal management system accessory parameters calculated by the thermal management system control algorithm.
The thermal management system control algorithm in this embodiment employs an adaptive fuzzy PID algorithm.
4) And in parallel, the diagnosis and monitoring task of the fuel cell thermal management system is distributed to a third core CPU2 of the multi-core processor, and the current temperature of the fuel cell is monitored and diagnosed through the CPU2, so as to judge whether to stop the operation of the fuel cell.
4.1 Acquiring a current temperature of the fuel cell;
4.2 If the current temperature of the fuel cell does not exceed the temperature early warning value, returning to the step of acquiring the current temperature of the fuel cell;
4.3 If the current temperature of the fuel cell exceeds the temperature early warning value, taking over the control of the fuel cell thermal management system;
4.4 The rotation speeds of the cooling fan and the water pump are adjusted to be maximum, and the thermostat is fully opened;
4.5 If the current temperature of the fuel cell does not exceed the threshold value, returning to the step of acquiring the current temperature of the fuel cell;
4.6 If the current temperature of the fuel cell exceeds the threshold, the fuel cell stops operating.
In order to facilitate the technical solution of the present invention to be better understood by the person skilled in the art, specific embodiments of the present invention are given as follows:
as shown in fig. 1, an embodiment of the present invention provides a method for optimizing and controlling a thermal management system of a fuel cell based on a multi-core chip, the method comprising the following steps:
1) The present embodiment uses a fuel cell controller of a three-core processor to distribute tasks of a fuel cell thermal management system to three cores CPU0, CPU1, CPU2 of the processor for parallel processing:
2) The fuel cell energy efficiency optimization method is distributed to a first core CPU0 of the multi-core processor in parallel;
2.1 Acquiring the ambient temperature T of the fuel cell in real time atm ；
2.2 Acquiring the load current I of the fuel cell in real time;
2.3 Acquiring the output power P of the fuel cell in real time st ；
2.4 Acquiring the consumed power P of accessories of a thermal management system such as a fan, a water pump, a thermostat and the like in real time c ；
2.5 Acquiring the current temperature T of the fuel cell in real time s ；
2.6 In the normal operating temperature range of the fuel cell, with the objective of maximizing the total energy efficiency, the present embodiment uses a genetic algorithm as the energy efficiency optimization algorithm. According to the data such as the ambient temperature, the load current, the current temperature of the fuel cell, the accessory consumption power and the like, calculating the optimal temperature value T in the current state on line optimal ；
The total energy efficiency is determined by the following formula:
，
in the above, P tot Representing total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed by accessories of the heat management system such as a cooling fan, a water pump, a thermostat and the like;
the optimal temperature value is optimized by a genetic algorithm as follows:
2.6.1 A) designing fitness function: the individual is decoded into load current magnitude, ambient temperature, and fuel cell temperature. Defining an objective function as an energy efficiency P tot Calculating the energy efficiency value P by using the variables tot And takes the same as the fitness of the individual. I.e. the effective value P tot The larger the individual, the higher the fitness.
2.6.2 The load current magnitude, the ambient temperature and the fuel cell temperature are coded in a binary manner;
2.6.3 Iterative optimization: repeating the algebra specified by the following steps:
2.6.3.1 For each individual, its fitness is calculated using a fitness function.
2.6.3.2 Selecting an excellent individual as a parent according to the fitness of the individual. The selection may use roulette selection, tournament selection, etc.
2.6.3.3 Cross operation): new individuals are created by exchanging gene fragments of the individuals. Single point crossover, multiple point crossover, etc. methods may be used.
2.6.3.3 Mutation operation: new changes are introduced by randomly altering the genes of the individual.
2.6.3.5 Updating the population: newly generated individuals are added to the new population.
2.6.4 Selecting the optimal individual: selecting the most suitable (energy efficiency P) tot Maximum) as the optimal individual.
2.6.5 Outputting the result: decoding the optimal individual into load current magnitude, ambient temperature and fuel cell temperature, and calculating the corresponding energy efficiency. The fuel cell temperature value corresponding to the optimal energy efficiency is the optimal temperature value, and specifically, the optimal temperature value is calculated to be 67.5 ℃ under a certain environment.
2.7 Judging the optimal temperature value T calculated by the energy efficiency optimization algorithm optimal Whether the normal operating temperature threshold of the fuel cell is exceeded or not, the normal operating temperature threshold may be set to 55 to 70 degrees celsius;
2.8 If the calculated optimal temperature value T optimal Writing the calculated optimal temperature value into a shared memory when the normal operating temperature threshold of the fuel cell is not exceeded;
2.9 If the calculated optimal temperature value exceeds the normal operating temperature threshold of the fuel cell, replacing the calculated optimal temperature value with a value within a preset normal operating temperature range, wherein the value within the preset normal operating temperature range can be set to 65 ℃; the preset temperature value T is set optimal Writing into a shared memory;
2.10 Triggering an interrupt, waiting for the CPU1 to read the shared memory data;
2.11 If the CPU1 finishes reading the shared memory data, clearing the interrupt;
2.12 A) enters the next cycle.
3) In parallel, the fuel cell thermal management system control algorithm is distributed to the second core CPU1 of the multi-core processor, and the following scheme is adopted in this embodiment for explanation:
3.1 Acquiring the current temperature T of the fuel cell s ；
3.2 Triggering an interrupt and starting timing;
3.3 CPU1 reads the real-time updated optimal temperature value T in the shared memory optimal ；
3.4 If the CPU0 does not update the data, waiting for the CPU0 to update the data;
3.5 If the timer wait exceeds 1ms, the last read optimal temperature value T is used ’ optimal Clearing the interrupt; (latency set to 1ms is just an example of an embodiment)
3.6 If CPU0 updates data in the timing time, reading the updated optimal temperature value T optimal The method comprises the steps of carrying out a first treatment on the surface of the Clearing the interrupt;
3.7 Ending the timer;
3.8 In this embodiment, the temperature is controlled by controlling the rotational speed of the fan by the adaptive fuzzy PID algorithm, the water pump is set at a fixed rotational speed, and the thermostat is set at a fixed opening.
The above-mentioned control scheme of controlling the rotation speed of the fan by the adaptive fuzzy PID algorithm, setting the water pump at a fixed rotation speed, and setting the thermostat at a fixed opening degree is merely exemplary, and the thermal management control of the fuel cell may also be performed according to other schemes, which is not limited in this application.
3.9 Input of the adaptive fuzzy PID algorithm is the real-time temperature of the fuel cell and the real-time updated optimal temperature value T optimal Difference, e (T) =t optimal (t)-T s (t)。
3.10 The output u (t) of the fuzzy PID algorithm is the fan speed, and the calculation formula is as follows: u (t) = (K) p +ΔK p )*e(t)+(K i +ΔK i )*∫e(t)dt+(K d +ΔK d )*de(t)/dt；
3.11 In the above, ΔK p ，ΔK i ，ΔK d The parameter K for PID is obtained by fuzzification, fuzzy reasoning and defuzzification for fuzzy control p ，Ki，K d Is a correction value of (2); k (K) p Is a proportionality coefficient, K i As integral coefficient, K d As a result of the differential coefficient,
e (t) is the temperature difference at the current time, jj e (t) dt is an integral of the temperature difference, and de (t)/dt is a differential of the error temperature difference.
3.12 Controlling the rotation speed of the cooling fan according to the rotation speed u (t) of the fan calculated by the control algorithm;
4) The tasks such as diagnosis and monitoring of the fuel cell thermal management system are distributed to the third core CPU2 of the multi-core processor for parallel;
4.1 Acquiring the current temperature T of the fuel cell s ；
4.2 If the current temperature of the fuel cell does not exceed the temperature early warning value, the temperature early warning value can be set to 68 ℃, and the step (4.1) is returned;
4.3 If the current temperature of the fuel cell exceeds the temperature early-warning value by 68 ℃, taking over the control of the fuel cell thermal management system;
4.4 The rotation speeds of the cooling fan and the water pump are adjusted to be maximum, and the thermostat is fully opened;
4.5 If the current temperature of the fuel cell does not exceed the threshold value, the temperature early warning value can be set to 70 ℃, and the step (4.1) is returned;
4.6 If the current temperature of the fuel cell exceeds the temperature early warning value of 70 ℃, the fuel cell stops working.
The thermal management system control algorithm of the present invention is not limited to a particular control method.
The fuel cell energy efficiency optimization algorithm of the present invention is not limited to a specific optimization method.
Factors that affect the energy efficiency of a fuel cell according to the present invention include, but are not limited to, ambient temperature, fuel cell temperature, etc.; other factors that affect energy efficiency may also be considered.
The task allocation of the multi-core processor used in the invention is not limited to three cores, and the task allocation of the fuel cell thermal management system to different cores for processors using different core numbers is also within the scope of the scheme.
The invention provides a method for distributing multiple algorithms to multiple cores in a multi-core processor, which can synchronously realize real-time tracking control of the temperature of a fuel cell and real-time optimization of energy efficiency of a fuel cell system, and particularly for simultaneously using multiple complex algorithms, the load of a controller can be reduced by the multi-core parallel method, the running time of the algorithms is reduced, and the real-time performance and accuracy of the control are ensured.
The functions which can be realized by the fuel cell thermal management system optimization control method based on the multi-core chip are all completed by computer equipment, the computer equipment comprises one or more processors and one or more memories, at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to realize the functions of the fuel cell thermal management system optimization control method based on the multi-core chip.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. An optimized control method of a fuel cell thermal management system based on a multi-core chip is characterized by comprising the following steps:
a fuel cell controller based on a multi-core processor distributes tasks of a fuel cell thermal management system to three cores CPU0, CPU1 and CPU2 of the processor in parallel;
the fuel cell energy efficiency optimization method is distributed to a first core CPU0 of the multi-core processor in parallel, and the optimal temperature value calculated by the energy efficiency optimization algorithm is written into a shared memory through the CPU 0;
distributing a fuel cell thermal management system control algorithm to a second core CPU1 of the multi-core processor in parallel, and continuously correcting a control target by reading an optimal temperature value updated in real time in a shared memory through the CPU 1;
and in parallel, the diagnosis and monitoring task of the fuel cell thermal management system is distributed to a third core CPU2 of the multi-core processor, and the current temperature of the fuel cell is monitored and diagnosed through the CPU2, so as to judge whether to stop the operation of the fuel cell.
2. The optimizing control method for a fuel cell thermal management system based on a multi-core chip according to claim 1, wherein the step of writing the optimal temperature value calculated by the energy efficiency optimizing algorithm into the shared memory by the CPU0 comprises:
acquiring the environmental temperature of the fuel cell in real time;
acquiring load current of the fuel cell in real time;
acquiring the output power of the fuel cell in real time;
acquiring the consumption power of a thermal management system accessory in real time, wherein the thermal management system accessory comprises a fan, a water pump and a thermostat;
acquiring the current temperature of the fuel cell in real time;
in the normal working temperature range of the fuel cell, with the aim of maximizing the total energy efficiency, calculating an optimal temperature value in the current state on line according to the environment temperature, the load current, the output power, the consumption power and the current temperature of the fuel cell through an energy efficiency optimization algorithm;
judging whether the optimal temperature value calculated by the energy efficiency optimization algorithm exceeds a normal working temperature threshold value of the fuel cell;
if the optimal temperature value does not exceed the normal working temperature threshold value of the fuel cell, writing the optimal temperature value into a shared memory;
triggering an interrupt to wait for the CPU1 to read the shared memory data;
if the CPU1 finishes reading the shared memory data, clearing the interrupt;
and entering the next cycle.
3. The optimal control method for a multi-core chip-based fuel cell thermal management system according to claim 2, wherein the total energy efficiency is determined by the following formula:
；
wherein P is tot Representing total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
4. The optimal control method for a multi-core chip-based fuel cell thermal management system according to claim 2, wherein the total energy efficiency is determined by the following formula:
；
wherein η represents total energy efficiency, P st For the output power of the fuel cell, P c The total power consumed for the thermal management system attachment.
5. The method for optimizing and controlling a fuel cell thermal management system based on a multi-core chip according to claim 2, wherein the energy efficiency optimization algorithm adopts a genetic algorithm.
6. The optimal control method for a multi-core chip-based fuel cell thermal management system according to claim 1, wherein the step of continuously correcting the control target by reading the optimal temperature value updated in real time in the shared memory by the CPU1 comprises:
acquiring the current temperature of the fuel cell;
triggering an interrupt and starting timing;
CPU1 reads the real-time updated optimal temperature value in the shared memory;
if the CPU0 does not update the data, waiting for the CPU0 to update the data;
if the preset time is waited, the last time of reading the optimal temperature value is used, and the interruption is cleared;
if the CPU0 updates the data in the timing time, reading the updated optimal temperature value, and clearing the interrupt;
ending the timing;
taking the read latest optimal temperature value as a temperature target for controlling the fuel cell, and calculating accessory parameters of a thermal management system by a thermal management system control algorithm, wherein the accessory parameters of the thermal management system comprise the rotating speed of a cooling fan, the rotating speed of a water pump and the opening degree of a thermostat;
and controlling the accessory of the thermal management system according to the thermal management system accessory parameters calculated by the thermal management system control algorithm.
7. The optimal control method for a fuel cell thermal management system based on a multi-core chip according to claim 6, wherein the thermal management system control algorithm adopts an adaptive fuzzy PID algorithm.
8. The optimal control method for a thermal management system of a fuel cell based on a multi-core chip according to claim 1, wherein the step of judging whether to stop the operation of the fuel cell by diagnosing and monitoring the current temperature of the fuel cell by the CPU2 comprises:
acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell does not exceed the temperature early warning value, returning to the step of acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell exceeds the temperature early warning value, taking over the control of the fuel cell thermal management system;
the rotation speeds of the cooling fan and the water pump are adjusted to be maximum, and the thermostat is fully opened;
if the current temperature of the fuel cell does not exceed the threshold value, returning to the step of acquiring the current temperature of the fuel cell;
if the current temperature of the fuel cell exceeds the threshold, the fuel cell stops operating.
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